Lab 02: Vector Basics
Cindy Lo
9/5/2018
About .RData files
Inspecting the data objects
# download RData file into your working directory
rdata <- "https://github.com/ucb-stat133/stat133-fall-2018/raw/master/data/nba2018-salary-points.RData"
download.file(url = rdata, destfile = 'nba2018-salary-points.RData')
# load data in your R session
load('nba2018-salary-points.RData')ls()## [1] "age" "experience" "player" "points1" "points2"
## [6] "points3" "position" "rdata" "salary" "scored"
## [11] "team"Using length() to explore the content of experience, player, and age
length(experience)## [1] 477length(player)## [1] 477length(age)## [1] 477Using head() to explore the content of experience, player, and age
head(experience)## [1] "9" "11" "6" "R" "9" "5"head(player)## [1] "Al Horford" "Amir Johnson" "Avery Bradley"
## [4] "Demetrius Jackson" "Gerald Green" "Isaiah Thomas"head(age)## [1] 30 29 26 22 31 27Using tail() to explore the content of experience, player, and age
tail(experience)## [1] "13" "R" "11" "2" "R" "15"tail(player)## [1] "Leandro Barbosa" "Marquese Chriss" "Ronnie Price" "T.J. Warren"
## [5] "Tyler Ulis" "Tyson Chandler"tail(age)## [1] 34 19 33 23 21 34Using summary() to explore the content of experience, player, and age
summary(experience)## Length Class Mode
## 477 character charactersummary(player)## Length Class Mode
## 477 character charactersummary(age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 19.00 23.00 26.00 26.39 29.00 40.00All of the chosen objects have the same length.
There doesn’t appear to be any missing values, i.e. NA in any of the objects.
To find out the class of each of the chosen objects:
class(experience)## [1] "character"class(player)## [1] "character"class(age)## [1] "integer"To know if the loaded objects is a vector, use is.vector()
is.vector(experience)## [1] TRUETo know if a given vector is of a certain data type, use typeof() which gives the type of storage of any object:
typeof(experience)## [1] "character"Manipulating Vectors: Subsetting
four <- head(player, n=4)
four## [1] "Al Horford" "Amir Johnson" "Avery Bradley"
## [4] "Demetrius Jackson"four[1]## [1] "Al Horford"four[0]## character(0)four[-1]## [1] "Amir Johnson" "Avery Bradley" "Demetrius Jackson"four[-c(1, 2, 3)]## [1] "Demetrius Jackson"four[5]## [1] NAfour[c(1, 2, 2, 3, 3, 3)]## [1] "Al Horford" "Amir Johnson" "Amir Johnson" "Avery Bradley"
## [5] "Avery Bradley" "Avery Bradley"Using seq(), rep() to extract:
All the even elements in player:
even_elem <- seq(from = 2, to = length(player), by = 2)
player[even_elem]## [1] "Amir Johnson" "Demetrius Jackson"
## [3] "Isaiah Thomas" "James Young"
## [5] "Jonas Jerebko" "Kelly Olynyk"
## [7] "Terry Rozier" "Andrew Bogut"
## [9] "Dahntay Jones" "Derrick Williams"
## [11] "Iman Shumpert" "James Jones"
## [13] "Kay Felder" "Kyle Korver"
## [15] "Larry Sanders" "Richard Jefferson"
## [17] "Bruno Caboclo" "Delon Wright"
## [19] "DeMarre Carroll" "Jakob Poeltl"
## [21] "Kyle Lowry" "Norman Powell"
## [23] "Pascal Siakam" "Serge Ibaka"
## [25] "Bradley Beal" "Chris McCullough"
## [27] "Danuel House" "Jason Smith"
## [29] "Kelly Oubre" "Markieff Morris"
## [31] "Tomas Satoransky" "DeAndre' Bembry"
## [33] "Dwight Howard" "Gary Neal"
## [35] "Kent Bazemore" "Lamar Patterson"
## [37] "Mike Dunleavy" "Paul Millsap"
## [39] "Taurean Waller-Prince" "Tim Hardaway"
## [41] "Gary Payton" "Greg Monroe"
## [43] "Jason Terry" "Khris Middleton"
## [45] "Matthew Dellavedova" "Miles Plumlee"
## [47] "Rashad Vaughn" "Steve Novak"
## [49] "Thon Maker" "Aaron Brooks"
## [51] "C.J. Miles" "Glenn Robinson"
## [53] "Joe Young" "Lance Stephenson"
## [55] "Monta Ellis" "Paul George"
## [57] "Rodney Stuckey" "Anthony Morrow"
## [59] "Cameron Payne" "Denzel Valentine"
## [61] "Isaiah Canaan" "Jimmy Butler"
## [63] "Michael Carter-Williams" "Paul Zipser"
## [65] "Rajon Rondo" "Dion Waiters"
## [67] "Hassan Whiteside" "Josh McRoberts"
## [69] "Justise Winslow" "Okaro White"
## [71] "Tyler Johnson" "Wayne Ellington"
## [73] "Andre Drummond" "Beno Udrih"
## [75] "Darrun Hilliard" "Ish Smith"
## [77] "Kentavious Caldwell-Pope" "Michael Gbinije"
## [79] "Reggie Jackson" "Tobias Harris"
## [81] "Brian Roberts" "Christian Wood"
## [83] "Frank Kaminsky" "Johnny O'Bryant"
## [85] "Marco Belinelli" "Michael Kidd-Gilchrist"
## [87] "Nicolas Batum" "Treveon Graham"
## [89] "Chasson Randle" "Derrick Rose"
## [91] "Justin Holiday" "Kyle O'Quinn"
## [93] "Marshall Plumlee" "Mindaugas Kuzminskas"
## [95] "Sasha Vujacic" "Aaron Gordon"
## [97] "Arinze Onuaku" "C.J. Watson"
## [99] "D.J. Augustin" "Elfrid Payton"
## [101] "Jeff Green" "Marcus Georges-Hunt"
## [103] "Nikola Vucevic" "Stephen Zimmerman"
## [105] "Alex Poythress" "Gerald Henderson"
## [107] "Jahlil Okafor" "Joel Embiid"
## [109] "Justin Harper" "Richaun Holmes"
## [111] "Sergio Rodriguez" "T.J. McConnell"
## [113] "Timothe Luwawu-Cabarrot" "Anthony Bennett"
## [115] "Brook Lopez" "Greivis Vasquez"
## [117] "Jeremy Lin" "Justin Hamilton"
## [119] "Luis Scola" "Randy Foye"
## [121] "Sean Kilpatrick" "Trevor Booker"
## [123] "Anderson Varejao" "Damian Jones"
## [125] "Draymond Green" "James Michael McAdoo"
## [127] "Kevin Durant" "Klay Thompson"
## [129] "Patrick McCaw" "Stephen Curry"
## [131] "Bryn Forbes" "David Lee"
## [133] "Dejounte Murray" "Joel Anthony"
## [135] "Kawhi Leonard" "LaMarcus Aldridge"
## [137] "Nicolas Laprovittola" "Pau Gasol"
## [139] "Bobby Brown" "Clint Capela"
## [141] "Isaiah Taylor" "Lou Williams"
## [143] "Nene Hilario" "Ryan Anderson"
## [145] "Trevor Ariza" "Alan Anderson"
## [147] "Blake Griffin" "Brice Johnson"
## [149] "DeAndre Jordan" "J.J. Redick"
## [151] "Luc Mbah a Moute" "Paul Pierce"
## [153] "Wesley Johnson" "Boris Diaw"
## [155] "Derrick Favors" "Gordon Hayward"
## [157] "Joe Ingles" "Joel Bolomboy"
## [159] "Rodney Hood" "Shelvin Mack"
## [161] "Alex Abrines" "Domantas Sabonis"
## [163] "Enes Kanter" "Josh Huestis"
## [165] "Nick Collison" "Russell Westbrook"
## [167] "Steven Adams" "Victor Oladipo"
## [169] "Brandan Wright" "Deyonta Davis"
## [171] "JaMychal Green" "Marc Gasol"
## [173] "Toney Douglas" "Troy Daniels"
## [175] "Wade Baldwin" "Zach Randolph"
## [177] "Allen Crabbe" "Damian Lillard"
## [179] "Evan Turner" "Jusuf Nurkic"
## [181] "Meyers Leonard" "Pat Connaughton"
## [183] "Alonzo Gee" "Darrell Arthur"
## [185] "Gary Harris" "Jameer Nelson"
## [187] "Juan Hernangomez" "Malik Beasley"
## [189] "Mike Miller" "Roy Hibbert"
## [191] "Wilson Chandler" "Anthony Davis"
## [193] "Dante Cunningham" "Donatas Motiejunas"
## [195] "Jarrett Jack" "Jrue Holiday"
## [197] "Omri Casspi" "Reggie Williams"
## [199] "Tim Frazier" "Ben Bentil"
## [201] "Devin Harris" "Dorian Finney-Smith"
## [203] "Harrison Barnes" "Jarrod Uthoff"
## [205] "Manny Harris" "Nicolas Brussino"
## [207] "Salah Mejri" "Wesley Matthews"
## [209] "Arron Afflalo" "Buddy Hield"
## [211] "Garrett Temple" "Jordan Farmar"
## [213] "Langston Galloway" "Rudy Gay"
## [215] "Ty Lawson" "Willie Cauley-Stein"
## [217] "Andrew Wiggins" "Cole Aldrich"
## [219] "John Lucas III" "Karl-Anthony Towns"
## [221] "Nemanja Bjelica" "Shabazz Muhammad"
## [223] "Zach LaVine" "Corey Brewer"
## [225] "David Nwaba" "Jordan Clarkson"
## [227] "Luol Deng" "Nick Young"
## [229] "Thomas Robinson" "Tyler Ennis"
## [231] "Alex Len" "Derrick Jones"
## [233] "Dragan Bender" "Eric Bledsoe"
## [235] "Jarell Eddie" "Leandro Barbosa"
## [237] "Ronnie Price" "Tyler Ulis"All the odd elements in salary:
odd_elem <- seq(from = 1, to = length(salary), by = 2)
salary[odd_elem]## [1] 26540100 8269663 1410598 6286408 4743000 1223653 3578880
## [8] 8000000 7806971 259626 5145 12800000 874636 21165675
## [15] 17638063 30963450 15330435 7330000 26540100 543471 14382022
## [22] 1921320 5300000 6050000 3730653 1200000 543471 15944154
## [29] 16957900 12000000 5893981 3386598 2708582 8400000 392478
## [36] 4000000 2500000 1015696 418228 3850000 51449 2995421
## [43] 5374320 12517606 925000 1403611 10500000 6348759 236457
## [50] 2368327 10230179 650000 8800000 1800000 4000000 2463840
## [57] 1052342 14153652 1453680 874636 23200000 1643040 1709720
## [64] 5782450 425000 13219250 15890000 4000000 874636 1227000
## [71] 543471 4000000 1015696 6500000 7000000 1704120 10991957
## [78] 4625000 2255644 2969880 375579 231521 5318313 6511628
## [85] 12000000 12250000 138938 6000000 24559380 11242000 17000000
## [92] 4317720 6191000 543471 543471 1375000 51449 17000000
## [99] 1209680 980431 17000000 6540000 3909840 31969 10000000
## [106] 2318280 442126 9424084 1514160 2993040 1015696 96969
## [113] 8550000 6088993 194494 1562280 1074145 980431 3333333
## [120] 1914544 1395600 726672 202300 11131368 1551659 1015696
## [127] 1403611 1182840 383351 5782450 2898000 10000000 543471
## [134] 2898000 874636 1192080 14000000 3578948 14445313 543471
## [141] 12385364 26540100 1045000 6000000 1720560 181969 11000000
## [148] 1551659 22868827 543471 13253012 1403611 1551659 10154495
## [155] 3940320 8000000 1015696 11000000 937800 2121288 2340600
## [162] 2183072 2483040 980431 4837500 247991 543471 8950000
## [169] 945000 22116750 2898000 1286160 26540100 5505618 4264057
## [176] 83119 7680965 3219579 6666667 600000 8988764 2751360
## [183] 1350120 15050000 3241800 3210840 150000 12078652 2328530
## [190] 1358500 3533333 4600000 543471 16957900 8081363 234915
## [197] 9904494 79922 11241218 650000 1015696 25000000 8375000
## [204] 4096950 680937 4384490 105498 2898000 8000000 4008882
## [211] 5229454 2202240 8046500 1439880 1188840 10661286 2022240
## [218] 3500000 2348783 3911380 3872520 13550000 1339680 5281680
## [225] 5332800 1034956 3267120 1551659 6191000 16000000 874636
## [232] 12606250 2223600 23069 10470000 469841 2941440 2128920
## [239] 12415000All multiples of 5 (e.g. 5, 10, 15, etc) of team:
by_5 <- seq(from = 5, to = length(team), by = 5)
team[by_5]## [1] BOS BOS BOS CLE CLE CLE TOR TOR TOR WAS WAS WAS ATL ATL ATL ATL MIL
## [18] MIL MIL IND IND IND IND CHI CHI CHI MIA MIA MIA DET DET DET CHO CHO
## [35] CHO NYK NYK NYK ORL ORL ORL PHI PHI PHI PHI BRK BRK BRK BRK GSW GSW
## [52] GSW SAS SAS SAS HOU HOU HOU LAC LAC LAC UTA UTA UTA OKC OKC OKC MEM
## [69] MEM MEM POR POR POR DEN DEN DEN NOP NOP NOP DAL DAL DAL DAL SAC SAC
## [86] SAC MIN MIN MIN LAL LAL LAL PHO PHO PHO
## 30 Levels: ATL BOS BRK CHI CHO CLE DAL DEN DET GSW HOU IND LAC LAL ... WASElements in positions 10, 20, 30, etc of scored:
by_10 <- seq(from = 10, to = length(scored), by = 10)
scored[by_10]## [1] 299 156 4 165 1779 1063 801 1143 577 322 630 291 538 98
## [15] 127 1321 780 1154 124 1046 64 559 1539 357 776 1999 1888 818
## [29] 936 1173 476 430 33 58 106 304 851 4 6 0 8 378
## [43] 681 2061 120 170 24even_elem_team <- seq(from = length(team), to = 2, by = -2)
team[even_elem_team]## [1] PHO PHO PHO PHO PHO PHO PHO PHO PHO LAL LAL LAL LAL LAL LAL LAL MIN
## [18] MIN MIN MIN MIN MIN MIN SAC SAC SAC SAC SAC SAC SAC SAC DAL DAL DAL
## [35] DAL DAL DAL DAL DAL DAL NOP NOP NOP NOP NOP NOP NOP NOP DEN DEN DEN
## [52] DEN DEN DEN DEN DEN POR POR POR POR POR POR POR MEM MEM MEM MEM MEM
## [69] MEM MEM MEM OKC OKC OKC OKC OKC OKC OKC UTA UTA UTA UTA UTA UTA UTA
## [86] UTA LAC LAC LAC LAC LAC LAC LAC HOU HOU HOU HOU HOU HOU HOU SAS SAS
## [103] SAS SAS SAS SAS SAS SAS GSW GSW GSW GSW GSW GSW GSW GSW BRK BRK BRK
## [120] BRK BRK BRK BRK BRK BRK BRK PHI PHI PHI PHI PHI PHI PHI PHI ORL ORL
## [137] ORL ORL ORL ORL ORL ORL ORL NYK NYK NYK NYK NYK NYK NYK NYK CHO CHO
## [154] CHO CHO CHO CHO CHO CHO DET DET DET DET DET DET DET MIA MIA MIA MIA
## [171] MIA MIA MIA CHI CHI CHI CHI CHI CHI CHI CHI IND IND IND IND IND IND
## [188] IND IND MIL MIL MIL MIL MIL MIL MIL MIL MIL MIL ATL ATL ATL ATL ATL
## [205] ATL ATL ATL WAS WAS WAS WAS WAS WAS WAS WAS TOR TOR TOR TOR TOR TOR
## [222] TOR CLE CLE CLE CLE CLE CLE CLE CLE CLE BOS BOS BOS BOS BOS BOS BOS
## 30 Levels: ATL BOS BRK CHI CHO CLE DAL DEN DET GSW HOU IND LAC LAL ... WASLogical Subsetting
Players in position of Center, of Warriors(GSW)
player[which(position == "C")]## [1] "Al Horford" "Kelly Olynyk" "Tyler Zeller"
## [4] "Andrew Bogut" "Channing Frye" "Edy Tavares"
## [7] "Larry Sanders" "Tristan Thompson" "Jakob Poeltl"
## [10] "Jonas Valanciunas" "Lucas Nogueira" "Daniel Ochefu"
## [13] "Ian Mahinmi" "Jason Smith" "Marcin Gortat"
## [16] "Dwight Howard" "Mike Muscala" "Greg Monroe"
## [19] "John Henson" "Miles Plumlee" "Thon Maker"
## [22] "Al Jefferson" "Myles Turner" "Cristiano Felicio"
## [25] "Joffrey Lauvergne" "Robin Lopez" "Hassan Whiteside"
## [28] "Udonis Haslem" "Willie Reed" "Andre Drummond"
## [31] "Aron Baynes" "Boban Marjanovic" "Cody Zeller"
## [34] "Frank Kaminsky" "Mike Tobey" "Joakim Noah"
## [37] "Kyle O'Quinn" "Marshall Plumlee" "Willy Hernangomez"
## [40] "Arinze Onuaku" "Bismack Biyombo" "Nikola Vucevic"
## [43] "Stephen Zimmerman" "Jahlil Okafor" "Joel Embiid"
## [46] "Richaun Holmes" "Shawn Long" "Tiago Splitter"
## [49] "Brook Lopez" "Justin Hamilton" "Anderson Varejao"
## [52] "Damian Jones" "David West" "JaVale McGee"
## [55] "Kevon Looney" "Zaza Pachulia" "Dewayne Dedmon"
## [58] "Joel Anthony" "Pau Gasol" "Chinanu Onuaku"
## [61] "Clint Capela" "Montrezl Harrell" "Nene Hilario"
## [64] "DeAndre Jordan" "Diamond Stone" "Marreese Speights"
## [67] "Jeff Withey" "Rudy Gobert" "Enes Kanter"
## [70] "Steven Adams" "Deyonta Davis" "Marc Gasol"
## [73] "Jusuf Nurkic" "Jarnell Stokes" "Mason Plumlee"
## [76] "Nikola Jokic" "Roy Hibbert" "Alexis Ajinca"
## [79] "Anthony Davis" "DeMarcus Cousins" "Omer Asik"
## [82] "A.J. Hammons" "Dwight Powell" "Nerlens Noel"
## [85] "Salah Mejri" "Georgios Papagiannis" "Kosta Koufos"
## [88] "Willie Cauley-Stein" "Cole Aldrich" "Jordan Hill"
## [91] "Karl-Anthony Towns" "Ivica Zubac" "Tarik Black"
## [94] "Timofey Mozgov" "Alan Williams" "Alex Len"
## [97] "Tyson Chandler"Players of either GSW (warriors) or LAL (lakers)
player[which(team == "GSW" | team == "LAL")]## [1] "Anderson Varejao" "Andre Iguodala" "Damian Jones"
## [4] "David West" "Draymond Green" "Ian Clark"
## [7] "James Michael McAdoo" "JaVale McGee" "Kevin Durant"
## [10] "Kevon Looney" "Klay Thompson" "Matt Barnes"
## [13] "Patrick McCaw" "Shaun Livingston" "Stephen Curry"
## [16] "Zaza Pachulia" "Brandon Ingram" "Corey Brewer"
## [19] "D'Angelo Russell" "David Nwaba" "Ivica Zubac"
## [22] "Jordan Clarkson" "Julius Randle" "Luol Deng"
## [25] "Metta World Peace" "Nick Young" "Tarik Black"
## [28] "Thomas Robinson" "Timofey Mozgov" "Tyler Ennis"Players in position Shooting Guard or Point Guards, of Lakers
player[which((position == "SG" | position == "PG") & team == "LAL")]## [1] "D'Angelo Russell" "David Nwaba" "Jordan Clarkson"
## [4] "Nick Young" "Tyler Ennis"Subset of Small Forwards of GSW and LAL
player[which((team == "GSW" | team == "LAL") & (position == "SF"))]## [1] "Andre Iguodala" "Matt Barnes" "Brandon Ingram"
## [4] "Corey Brewer" "Luol Deng" "Metta World Peace"Name of the player with the largest salary
player[which.max(salary)] #or player[salary == max(salary)]## [1] "LeBron James"Name of the palyer with the smallest salary
player[which.min(salary)] #or player[salary == min(salary)]## [1] "Edy Tavares"Name of the player with the largest number of scored points
player[which.max(scored)]## [1] "Russell Westbrook"Salary of the player with the largest number of points
salary[player == player[which.max(scored)]]## [1] 26540100Largest salary of all the Centers
max(salary[which(position == "C")])## [1] 26540100Team of the player with the largest number of scored points
team[which(player == player[which.max(scored)])]## [1] OKC
## 30 Levels: ATL BOS BRK CHI CHO CLE DAL DEN DET GSW HOU IND LAC LAL ... WASName of the player with the largest number of 3-pointers
player[which.max(points3)]## [1] "Stephen Curry"Subsetting with Character Vectors
Some plotting
#install.packages(c("ggplot2", "plotly"))
library(ggplot2)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplot_ly(x = scored, y = salary, type = "scatter", mode = "markers")log_scored <- log(scored)
log_salary <- log(salary)
plot(log_scored, log_salary)
text(log_scored, log_salary, labels = abbreviate(player))
Create a scatterplot of points and salary for the Warriors (GSW), displaying the names of the players. Generate two scatter plots, one with raw values(original scale, and another plot with log-transformations).
plot_ly(x = scored[which(team == "GSW")], y = salary[which(team == "GSW")], type = "scatter", mode = "markers")gsw_log_scored <- log(scored[which(team == "GSW")])
gsw_log_salary <- log(salary[which(team == "GSW")])
plot(gsw_log_scored, gsw_log_salary)
text(gsw_log_scored, gsw_log_salary, labels = player[which(team == "GSW")])
Recycling
Create a new vector salary_millions with the converted values in millions of dollars
salary_millions <- salary / 1000000
salary_millions## [1] 26.540100 12.000000 8.269663 1.450000 1.410598 6.587132 6.286408
## [8] 1.825200 4.743000 5.000000 1.223653 3.094014 3.578880 1.906440
## [15] 8.000000 0.242224 7.806971 0.024022 0.259626 0.402043 0.005145
## [22] 9.700000 12.800000 1.551659 0.874636 0.543471 21.165675 5.239437
## [29] 17.638063 0.207722 30.963450 2.500000 15.330435 1.589640 7.330000
## [36] 1.577280 26.540100 14.200000 0.543471 2.703960 14.382022 12.000000
## [43] 1.921320 0.874636 5.300000 1.196040 6.050000 12.250000 3.730653
## [50] 22.116750 1.200000 1.191480 0.543471 0.543471 15.944154 5.000000
## [57] 16.957900 2.006640 12.000000 7.400000 5.893981 2.870813 3.386598
## [64] 1.499760 2.708582 23.180275 8.400000 0.072193 0.392478 15.730338
## [71] 4.000000 0.128623 2.500000 4.837500 1.015696 20.072033 0.418228
## [78] 2.318280 3.850000 2.281605 0.051449 0.035166 2.995421 17.100000
## [85] 5.374320 1.551659 12.517606 15.200000 0.925000 9.607500 1.403611
## [92] 12.500000 10.500000 1.811040 6.348759 1.551659 0.236457 2.568600
## [99] 2.368327 2.700000 10.230179 4.583450 0.650000 1.050500 8.800000
## [106] 1.052342 1.800000 4.000000 4.000000 10.770000 2.463840 18.314532
## [113] 1.052342 7.000000 14.153652 3.488000 1.453680 2.112480 0.874636
## [120] 2.092200 23.200000 1.015696 1.643040 17.552209 1.709720 3.183526
## [127] 5.782450 0.750000 0.425000 14.000000 13.219250 2.898000 15.890000
## [134] 22.116750 4.000000 5.782450 0.874636 2.593440 1.227000 0.374933
## [141] 0.543471 5.628000 4.000000 6.000000 1.015696 22.116750 6.500000
## [148] 1.551659 7.000000 0.874636 1.704120 6.000000 10.991957 3.678319
## [155] 4.625000 0.650000 2.255644 14.956522 2.969880 17.200000 0.375579
## [162] 1.050961 0.231521 0.874636 5.318313 2.730000 6.511628 0.276827
## [169] 12.000000 6.333333 12.250000 13.000000 0.138938 20.869566 6.000000
## [176] 0.543471 24.559380 0.243860 11.242000 21.323250 17.000000 1.015696
## [183] 4.317720 3.900000 6.191000 0.543471 0.543471 2.898000 0.543471
## [190] 1.410598 1.375000 4.351320 0.051449 0.440078 17.000000 5.000000
## [197] 1.209680 7.250000 0.980431 2.613600 17.000000 15.000000 6.540000
## [204] 0.031969 3.909840 11.750000 0.031969 0.950000 10.000000 0.038903
## [211] 2.318280 9.000000 0.442126 4.788840 9.424084 4.826160 1.514160
## [218] 0.057672 2.993040 1.025831 1.015696 8.000000 0.096969 0.874636
## [225] 8.550000 1.326960 6.088993 1.015696 0.194494 21.165675 1.562280
## [232] 4.347826 1.074145 11.483254 0.980431 3.000000 3.333333 5.000000
## [239] 1.914544 2.500000 1.395600 0.980431 0.726672 9.250000 0.202300
## [246] 1.551659 11.131368 1.171560 1.551659 15.330435 1.015696 0.980431
## [253] 1.403611 26.540100 1.182840 16.663575 0.383351 0.543471 5.782450
## [260] 12.112359 2.898000 0.543471 10.000000 1.551659 0.543471 1.180080
## [267] 2.898000 0.663810 0.874636 17.638063 1.192080 20.575005 14.000000
## [274] 0.210995 3.578948 15.500000 14.445313 0.934293 0.543471 1.296240
## [281] 12.385364 0.305000 26.540100 7.000000 1.045000 2.898000 6.000000
## [288] 18.735364 1.720560 7.806971 0.181969 1.315448 11.000000 20.140838
## [295] 1.551659 1.273920 22.868827 21.165675 0.543471 7.377500 13.253012
## [302] 2.203000 1.403611 3.500000 1.551659 5.628000 10.154495 7.000000
## [309] 3.940320 11.050000 8.000000 16.073140 1.015696 2.250000 11.000000
## [316] 0.600000 0.937800 1.406520 2.121288 2.433334 2.340600 5.994764
## [323] 2.183072 2.440200 2.483040 17.145838 0.980431 1.191480 4.837500
## [330] 3.750000 0.247991 26.540100 0.543471 3.140517 8.950000 6.552960
## [337] 0.945000 5.700000 22.116750 1.369229 2.898000 0.980431 1.286160
## [344] 21.165675 26.540100 0.603558 5.505618 3.332940 4.264057 1.793760
## [351] 0.083119 10.361445 7.680965 18.500000 3.219579 24.328425 6.666667
## [358] 16.393443 0.600000 1.921320 8.988764 9.213484 2.751360 0.874636
## [365] 1.350120 0.495227 15.050000 8.070175 3.241800 1.655880 3.210840
## [372] 4.540525 0.150000 1.987440 12.078652 1.627320 2.328530 3.500000
## [379] 1.358500 5.000000 3.533333 11.200000 4.600000 22.116750 0.543471
## [386] 2.978250 16.957900 0.576724 8.081363 0.091274 0.234915 11.286518
## [393] 9.904494 3.000000 0.079922 0.252676 11.241218 2.090000 0.650000
## [400] 0.031969 1.015696 4.228000 25.000000 0.543471 8.375000 22.116750
## [407] 4.096950 0.063938 0.680937 0.119494 4.384490 0.543471 0.105498
## [414] 0.874636 2.898000 17.100000 8.000000 12.500000 4.008882 3.517200
## [421] 5.229454 8.000000 2.202240 0.082979 8.046500 5.200000 1.439880
## [428] 13.333333 1.188840 1.315448 10.661286 3.551160 2.022240 6.006600
## [435] 3.500000 7.643979 2.348783 0.635753 3.911380 5.960160 3.872520
## [442] 3.800000 13.550000 3.046299 1.339680 2.240880 5.281680 7.600000
## [449] 5.332800 0.137466 1.034956 12.500000 3.267120 18.000000 1.551659
## [456] 5.443918 6.191000 1.050961 16.000000 1.733880 0.874636 4.823621
## [463] 12.606250 0.543471 2.223600 4.276320 0.023069 14.000000 10.470000
## [470] 0.102898 0.469841 4.000000 2.941440 0.465143 2.128920 0.918369
## [477] 12.415000Take the values in points, points2, and points3 and create a new vector scored_points that gives you the same values as scored
scored_points <- points1 + (2 * points2) + (3 * points3)
scored_points## [1] 952 520 894 10 262 2199 999 68 515 299 38 678 835 410
## [15] 178 0 676 9 179 156 6 567 351 132 161 166 1142 373
## [29] 1816 4 1954 448 630 14 740 150 2020 638 107 165 959 1344
## [43] 253 636 139 229 445 327 330 1779 81 1 24 0 173 420
## [57] 1805 501 883 1063 1075 154 285 101 1414 1002 270 4 61 801
## [71] 257 9 391 169 435 1246 25 335 444 1143 0 20 1832 951
## [85] 1025 307 392 426 767 577 528 82 451 142 83 5 0 226
## [99] 683 322 535 815 21 419 1254 68 232 43 177 630 1173 1775
## [113] 59 281 814 41 437 54 316 291 1096 181 370 1816 89 297
## [127] 744 240 0 538 839 729 1483 1309 975 107 539 196 324 98
## [141] 497 1002 31 648 374 1105 365 227 191 127 60 758 767 1047
## [155] 1105 4 141 752 339 1321 1 142 50 35 639 874 603 18
## [169] 1830 780 849 743 2 1164 312 57 1659 95 835 1154 232 629
## [183] 1196 496 275 40 98 425 215 124 587 1019 9 4 483 281
## [197] 21 616 82 1046 1167 638 327 14 317 1096 0 23 299 64
## [211] 1040 662 171 590 33 627 203 12 756 559 864 530 148 556
## [225] 39 445 30 115 95 1539 468 7 543 523 428 442 126 184
## [239] 209 357 675 919 432 709 54 18 574 19 316 776 527 147
## [253] 472 1555 135 1742 114 282 389 1999 426 94 497 576 303 130
## [267] 387 25 483 1888 246 1243 517 59 759 792 638 62 14 818
## [281] 1217 3 2356 343 527 609 639 979 504 936 58 86 889 1316
## [295] 292 4 1104 1029 10 1173 1008 484 711 81 538 186 283 338
## [309] 412 476 829 1601 146 581 715 22 100 748 1137 430 440 406
## [323] 522 479 145 1033 421 14 88 33 43 2558 183 905 207 1067
## [337] 425 189 210 58 429 689 165 1446 1415 117 643 551 586 106
## [351] 55 1028 532 845 1837 2024 200 586 78 304 773 401 327 98
## [365] 218 11 1145 262 603 851 811 687 3 305 587 83 245 28
## [379] 1221 4 820 1117 207 2099 87 435 414 150 700 6 267 1029
## [393] 85 12 52 27 563 464 48 0 8 437 769 350 516 1518
## [407] 381 40 106 8 188 150 35 213 898 986 461 515 495 378
## [421] 900 506 124 12 470 114 79 562 289 681 163 611 63 1933
## [435] 197 105 816 2 12 2061 293 403 836 772 209 889 740 129
## [449] 984 120 284 1205 975 425 57 791 383 241 401 170 346 613
## [463] 595 168 1726 146 3 1390 434 24 7 419 753 14 951 444
## [477] 397Factors
is.factor(team)## [1] TRUEposition_fac <- factor(position)
position_fac[1:5]## [1] C PF SG PG SF
## Levels: C PF PG SF SGtable(position_fac)## position_fac
## C PF PG SF SG
## 97 98 96 84 102Practice manipulating position_fac to get:
Positions of Warriors
position_fac[which(team == "GSW")]## [1] C SF C C PF SG PF C PF C SG SF SG PG PG C
## Levels: C PF PG SF SGPositions of players with salaries > 15 millions
position_fac[which(salary_millions > 15)]## [1] C PF PG SF C SG SG C PG C SF PF C SF SF SG SF PG C C PF SG SF
## [24] PG C C SG C PF PF SG SF PF C PG PF PF PG C SF C PG SF C PG SG
## [47] PG SF SF C C PF PF SG SF C
## Levels: C PF PG SF SGFrequencies (counts) of positions with salaries > 15 millions
table(position_fac[which(salary_millions > 15)])##
## C PF PG SF SG
## 17 10 9 12 8Relative frequencies (proportions) of Shooting Guards (‘SG’) in each team
team_fac <- factor(team)
table(team_fac[position == "SG"]) / table(team_fac)##
## ATL BOS BRK CHI CHO CLE DAL
## 0.1764706 0.2000000 0.2105263 0.3125000 0.3125000 0.2222222 0.2222222
## DEN DET GSW HOU IND LAC LAL
## 0.2352941 0.2000000 0.1875000 0.2142857 0.1250000 0.2000000 0.2142857
## MEM MIA MIL MIN NOP NYK OKC
## 0.1875000 0.2857143 0.2105263 0.1428571 0.1250000 0.2666667 0.1333333
## ORL PHI PHO POR SAC SAS TOR
## 0.2777778 0.1764706 0.2941176 0.2307692 0.3125000 0.2500000 0.1333333
## UTA WAS
## 0.1333333 0.2000000More plots
plot(scored, salary, col = position_fac)
plot(scored, salary, col = position_fac, pch = 3)
plot(scored, salary, col = position_fac, pch = 1)
plot(scored, salary, col = position_fac, pch = 3, cex = 3)
plot(scored, salary, col = position_fac, pch = 3, cex = 10)
plot(scored, salary, col = position_fac, pch = 3, cex = 0.4)
plot(scored, salary, col = position_fac, pch = 3, cex = 0.1)
plot(scored, salary, col = position_fac, pch = 3, cex = 0.4, xlab = "scores", ylab = "salary in millions")